The Study of a Sales Forecast Model Based on SA-LSTM

Abstract

Sales forecast is an indispensable link in the business activities of enterprises, and the accuracy of prediction is directly related to the effectiveness of enterprise sales and operation activities. In order to improve the prediction accuracy, a sales forecasting model based on LSTM is proposed. The model uses SA to optimize the initial connection weights of LSTM neural network, which solves the problem that the LSTM neural network converges to the local optimal, thus improving the network performance, and then makes an empirical analysis of the construction of the sales forecasting model based on SA-LSTM. The results show that the sales forecasting model improves the prediction accuracy, also reduces the number of iterations, and obtains a good prediction effect.